Search

Motivation We have previously designed and implemented a tree-based ontology to represent glycan structures with the aim of searching these structures with a glyco-driven syntax. This resulted in creating the GlySTreeM knowledge-base as a linchpin of the structural matching procedure and we now introduce a query language, called GlycoQL, for the actual implementation of a glycan structure search. Results The methodology is described and illustrated with a use-case focused on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spike protein glycosylation. We show how to enhance site annotation with federated queries involving UniProt and GlyConnect, our glycoprotein database. Availability and implementation https://glyconnect.expasy.org/glycoql/. [ABSTRACT FROM AUTHOR]

Recent years have seen great advances in the development of glycoproteomics protocols and methods resulting in a sustainable increase in the reporting proteins, their attached glycans and glycosylation sites. However, only very few of these reports find their way into databases or data repositories. One of the major reasons is the absence of digital standard to represent glycoproteins and the challenging annotations with glycans. Depending on the experimental method, such a standard must be able to represent glycans as complete structures or as compositions, store not just single glycans but also represent glycoforms on a specific glycosylation side, deal with partially missing site information if no site mapping was performed, and store abundances or ratios of glycans within a glycoform of a specific site. To support the above, we have developed the GlycoConjugate Ontology (GlycoCoO) as a standard semantic framework to describe and represent glycoproteomics data. GlycoCoO can be used to represent glycoproteomics data in triplestores and can serve as a basis for data exchange formats. The ontology, database providers and supporting documentation are available online (https://github.com/glycoinfo/GlycoCoO). [ABSTRACT FROM AUTHOR]

Mammalian glycosaminoglycans are linear complex polysaccharides comprising heparan sulfate, heparin, dermatan sulfate, chondroitin sulfate, keratan sulfate and hyaluronic acid. They bind to numerous proteins and these interactions mediate their biological activities. GAG–protein interaction data reported in the literature are curated mostly in MatrixDB database (http://matrixdb.univ-lyon1.fr/). However, a standard nomenclature and a machine-readable format of GAGs together with bioinformatics tools for mining these interaction data are lacking. We report here the building of an automated pipeline to (i) standardize the format of GAG sequences interacting with proteins manually curated from the literature, (ii) translate them into the machine-readable GlycoCT format and into SNFG (Symbol Nomenclature For Glycan) images and (iii) convert their sequences into a format processed by a builder generating three-dimensional structures of polysaccharides based on a repertoire of conformations experimentally validated by data extracted from crystallized GAG–protein complexes. We have developed for this purpose a converter (the CT23D converter) to automatically translate the GlycoCT code of a GAG sequence into the input file required to construct a three-dimensional model. [ABSTRACT FROM AUTHOR]

Nowadays, due to the advance of experimental techniques in glycomics, large collections of glycan profiles are regularly published. The rapid growth of available glycan data accentuates the lack of innovative tools for visualizing and exploring large amount of information. Scientists resort to using general-purpose spreadsheet applications to create ad hoc data visualization. Thus, results end up being encoded in publication images and text, while valuable curated data is stored in files as supplementary information. To tackle this problem, we have built an interactive pipeline composed with three tools: Glynsight, EpitopeXtractor and Glydin'. Glycan profile data can be imported in Glynsight, which generates a custom interactive glycan profile. Several profiles can be compared and glycan composition is integrated with structural data stored in databases. Glycan structures of interest can then be sent to EpitopeXtractor to perform a glycoepitope extraction. EpitopeXtractor results can be superimposed on the Glydin' glycoepitope network. The network visualization allows fast detection of clusters of glycoepitopes and discovery of potential new targets. Each of these tools is standalone or can be used in conjunction with the others, depending on the data and the specific interest of the user. [ABSTRACT FROM AUTHOR]

Issaku Yamada SP 1,2 sp , Matthew P. Campbell SP 3 sp , Nathan Edwards SP 3 sp , Leyla Jael Castro SP 4 sp , Frederique Lisacek SP 5 sp , Julien Mariethoz SP 6 sp , Tamiko Ono SP 7 sp , Rene Ranzinger SP 8 sp , Daisuke Shinmachi SP 9 sp and Kiyoko F. Aoki-Kinoshita SP 10 sp SP 2 sp Research Depertment, The Noguchi Institute, 1-9-7 Kaga, Itabashi, Tokyo 173-0003, Japan, SP 3 sp Institute for Glycomics, Griffith University at Gold Coast, Southport, QLD 4215, Australia, SP 4 sp Department of Biochemistry, Molecular and Cellular Biology, Georgetown University Medical Center, Washington, D.C. 20007, USA, SP 5 sp ZB MED Information Centre for Life Sciences, Gleueler Str. 60, 50931 Cologne, Germany, SP 6 sp Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Computer Science Department, University of Geneva, route de Drize 7, CH - 1227 Geneva Switzerland, and also Section of Biology, University of Geneva, Geneva, Switzerland, SP 7 sp Proteome Informatics Group, SIB Swiss Institute of Bio informatics, 7 Route de Drize, 1227 Geneva, Switzerland, SP 8 sp Faculty of Science and Engineering, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan, SP 9 sp Complex Carbohydrate Research Center, The University of Georgia, 315 Riverbend Rd, Athens, Georgia 30602, USA, SP 10 sp R&D Department, SparqLite LLC. [Extracted from the article]

Summary: Sequencing oligosaccharides by exoglycosidases, either sequentially or in an array format, is a powerful tool to unambiguously determine the structure of complex N- and O-link glycans. Here, we introduce GlycoDigest, a tool that simulates exoglycosidase digestion, based on controlled rules acquired from expert knowledge and experimental evidence available in GlycoBase. The tool allows the targeted design of glycosidase enzyme mixtures by allowing researchers to model the action of exoglycosidases, thereby validating and improving the efficiency and accuracy of glycan analysis.Availability and implementation: http://www.glycodigest.org.Contact: matthew.campbell@mq.edu.au or frederique.lisacek@isb-sib.ch [ABSTRACT FROM AUTHOR]